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Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel‐frequency cepstral coefficients (MFCCs) and back‐propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real‐world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over‐fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real‐time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real‐world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real‐time monitoring of bridges, ultimately leading to safer and longer‐lasting infrastructure. [ABSTRACT FROM AUTHOR]